Instructions to use hf-internal-testing/tiny-random-SeamlessM4Tv2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SeamlessM4Tv2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-SeamlessM4Tv2Model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SeamlessM4Tv2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SeamlessM4Tv2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1883f64be3dfd5c9b113d7bc0a6608609ecc55e4efa848356bbb548084a68b3c
- Size of remote file:
- 338 kB
- SHA256:
- 7d245fdb0135983a0f08e6f92dbbeb2af4050256f83acd018362e2c70b65b073
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